tbuckley/cabot-search huggingface dataset:
Racial differences in muscle measures and survival among older adults with GI malignancies.
Authors: Gabriel Aleixo, Daniel L. Hess, Smith Giri, Grant Richard Williams
Journal: Journal of Clinical Oncology (2023)

12044 Background: Sarcopenia (low muscle mass) and myosteatosis (fat infiltration of muscle) can be assessed from routinely obtained Computed tomography (CT) images and are independent predictors of chemotherapy toxicities and survival among patients with gastrointestinal (GI) malignancies. However, most studies to date have consisted of predominantly white populations. Therefore, the role of sarcopenia and myosteatosis among racial minority populations is unknown. Our objective was to examine racial differences in muscle measures and their association with survival among older adults with GI malignancies. Methods: This is a prospective cohort study of older adults with GI cancers from the University of Alabama at Birmingham. Single slices of axial CT images from L3 segments were analyzed using Slice-O-Matic software. Skeletal muscle area (cm 2 ) was divided by height to obtain the skeletal muscle index (SMI, cm 2 /m 2 ). Skeletal muscle radiodensity (SMD) in Hounsfield units (HU) was used to approximate muscle composition. Sarcopenia (low SMI) and myosteatosis (low SMD) were defined based on standard cut-offs developed in a cohort of GI malignancies (Martin et al., JCO 2013). We compared differences in muscle measures between non-Hispanic (NH) White and NH Black participants. We utilized Cox models, separated by race, to examine the impact of muscle measures on survival. We adjusted for age, sex, education, cancer type, cancer stage, lines of therapy, and comorbidities. Results: Six hundred sixty patients were included, 76% NH White and 24% NH Black. The median age was 69.4 years, 35% had colorectal cancer, and 42% had stage IV disease. NH Black patients had higher SMD (41.7 vs. 38.3 HU; p &lt; 0.001) and lower rates of myosteatosis (40% vs. 27%; p = 0.04) but similar SMI (42.3 vs. 41.1 cm 2 /m 2 ) and rates of sarcopenia (58% vs. 55%). In stratified Cox models, each 5 HU unit increase in SMD was associated with reduced risk of all-cause mortality of NH black patients (HR 0.88; 95% CI 0.80-0.98 p = 0.02) and not in NH White patients (HR 0.95; 95% CI-0.88-1.01; p = 0.14). Meanwhile, each 5-unit increase in SMI was associated with improved survival in NH White patients (HR 0.91; CI 0.85-0.98; p = 0.03) but not in NH Black patients (HR 0.89 CI0.78-1.03 p = 0.13). When using existing cut points for sarcopenia and myosteatosis, NH White patients with sarcopenia had worse survival (HR 1.42; CI 1.04-1.92; p = 0.02), but sarcopenia among NH black patients was not associated with worse survival (HR 1.16 95% CI 0.62-2.16 p = 0.64). Myosteatosis was not associated with worse survival in either NH white (HR 95%CI 0.95-1.68 p = 0.10) or NH black patients (HR 1.61 95%CI 0.88-2.95 p = 0.12). Conclusions: Muscle measures may differ by race and have different associations with survival by racial group. Therefore, it is essential to develop cut points for muscle measures incorporating race.
Identification of cytokines associated with response and cytokine release syndrome: Analysis of MagnetisMM-3 cohort A.
Authors: Hang Quach, Nizar J. Bahlis, Paula Rodríguez-Otero, Andrea Viqueira, Shen-Wu Wang, Sangeetha Sathaiah, Douglas M Robinson, Thomas O'Brien, Katja Weisel
Journal: Journal of Clinical Oncology (2023)

8044 Background: MagnetisMM-3 (NCT04649359) is a phase 2 study of elranatamab monotherapy in patients with multiple myeloma refractory to at least 1 proteasome inhibitor, 1 immunomodulatory drug, and 1 anti-CD38 antibody. Promising efficacy and safety have been observed in patients who were naïve to B-cell maturation antigen (BCMA)-directed therapy (Cohort A) in MagnetisMM-3 (Bahlis et al., ASH 2022). Methods: In MagnetisMM-3, patients received subcutaneous elranatamab in 28-day cycles with step-up doses of 12 mg on C1D1 and 32 mg on C1D4 followed by 76 mg QW beginning C1D8. Serum samples were collected on C1D1 prior to first priming dose, and on C1D2, pre-dose C1D4, C1D5, pre-dose C1D8, C1D15, and C1D22. Levels of 45 peripheral cytokines were analyzed by proximity extension assay and a longitudinal mixed effects model was applied to each cytokine separately. Cytokines with a significant 2-fold differential expression (responders [defined as patients with best overall response of very good partial response or better] vs non-responders [defined as patients with best overall response of partial response or worse]; cytokine release syndrome [CRS] vs no CRS) at any timepoint are reported. Clinical data cutoff was in Oct 2022 with median follow up of 10.4 months. Results: Baseline levels of IL-6 (2.6-fold), IL-17C (2.0-fold), and MIP-1α (2.1-fold) were lower in patients who achieved a response. After the first priming dose, 13 cytokines in the panel, including CCL8, IFN-γ, IL-2, and IL-27, were differentially expressed in responders vs non-responders. At baseline, no cytokines were found to predict CRS. After the first priming dose, 18 cytokines in the panel, including CCL8, IFN-γ, IL-10, IL-2, IL-27, IL-6, and IL-17A, were differentially expressed in patients with CRS vs no CRS. The maximal differential expression for 17/18 cytokines occurred at C1D2, ~24 hours post the first priming dose. Conclusions: Lower baseline levels of IL-6, IL-17C, and MIP-1α correlated with a response of VGPR or better in BCMA-naïve patients from MagnetisMM-3, suggesting that lower levels of these cytokines might reflect a favorable immune environment. Many cytokines were differentially expressed at higher levels in patients who experienced CRS vs those who did not; the most prominent cytokines included CCL-8, IFN-γ, and IL-10. The timing of induction of CCL-8, IFN-γ, and IL-10 occurred by C1D2 suggesting a potential contribution of these cytokines in driving CRS. Clinical trial information: NCT04649359 .
Radiomics to predict checkpoint inhibitor pneumonitis based on chest CT scans in patients with advanced cancer.
Authors: Wim Vos, François Cousin, Mathieu Wendling, Pierre Freres, Julien Guiot, Mariaelena Occhipinti, Roland Hustinx
Journal: Journal of Clinical Oncology (2023)

e20550 Background: Checkpoint inhibitor pneumonitis (CIP) is a concerning immune-related adverse event responsible for treatment discontinuation, hospitalization, with acknowledged possible fatal cases. The aim of our study was to assess the potential role of machine learning models, combining pre-treatment CT lung radiomic features with clinical data, to predict the risk of developing CIP. Methods: A cohort of 116 patients with different type advanced malignancies (81% lung cancer, 14% melanoma, 8% other solid tumors) treated with immune checkpoint inhibitors (ICIs) were retrospectively enrolled in this study and divided into a CIP (n = 35) and a control group (n = 81). Each patients had a chest CT scan at baseline and associated clinical covariates. Lungs and lobes were segmented semi-automatically on pre-treatment CT scans. We extracted radiomics features and selected them by the Minimum Redundancy Maximum Relevance (mRMR) method. Clinical-only, radiomics-only, and clinical-radiomics models (both lung- and lobe-based) were built using generalized linear (GLM) and random forest (RF) models to predict the occurrence of CIP. A nested 5-fold cross-validation scheme was used in the training, validation and test sets. Model performances were assessed in terms of Area under the Curve (AUC). Results: The most accurate clinical (RF) and the best radiomic (lobe-based GLM) models achieved AUCs of 0.72 (95%CI:0.51-0.92) and 0.71 (95%CI:0.58-0.84) on the test set, respectively. No statistically significant differences have been found between lung- and lobe-based radiomic models. The RF model combining lung-based radiomic and clinical features showed the best performances overall with an AUC of 0.75 (95%CI:0.62-0.88) on the test set. Conclusions: CT-based lung radiomic, and clinical models alone show moderate to good performances at identifying patients at risk of developing CIP. Nevertheless, we demonstrated the added value of machine learning models built combining clinical and radiomic features to better stratify the risk of developing CIP in patients with advanced malignancies treated with ICIs.
AI to characterize patients with lung or ovarian cancer and those with cachexia for discovery of undiagnosed, misdiagnosed, and miscoded patients at scale in a privacy-preserving manner.
Authors: Yike Guo, Jingqing Zhang, Marcin Burda, Parastoo Falakaflaki, Atri Vivek Sharma, Vibhor Gupta
Journal: Journal of Clinical Oncology (2023)

10627 Background: Discovering undiagnosed, misdiagnosed or miscoded patients is critical to ensure suitability for clinical care pathways and for maximizing clinical trial recruitment. Meta-data (e.g. ICD) and generic NLP based approaches fall short due to: lack of scalability, significant miscoding of patient records and searching a pre-empted list of features without understanding their context or relationships between them. This study addresses these limitations through the application of Pangaea’s novel AI product (PIES) to characterize patients, which has discovered 4x more undiagnosed, misdiagnosed and miscoded patients in a scalable and privacy-preserving manner. Methods: PIES was configured on 235 clinically validated lung, ovarian and cachectic cancer patient records from secondary care. First, PIES determines all concepts which are reflected in a patient’s record based on a medical knowledge base. Second, PIES determines key clinical features and actionable relationships between them which help stratify the target patient population. Third, the findings from steps 1 and 2, are validated by clinicians and all feedback is used to refine these steps and evolve the results through a continuous loop. Following these, PIES’s characterization of the target patient population is applied across clinically validated datasets for evaluation and then across multiple healthcare systems behind their firewalls to ensure scalability and privacy compliance. Results: Applied to a dataset of 100 clinically validated patients, ICD codes correctly discovers 48 lung cancer patients and misses 18 patients (i.e. who had a diagnosis of lung cancer but were miscoded). By contrast, PIES discovers 63 lung cancer patients and misses only 3 patients. Similarly, ICD codes correctly discovers 36 ovarian cancer patients and misses 16 patients in a dataset of 100 clinically validated patients. PIES correctly discovers all 52 ovarian cancer patients. Separately, in a dataset of 235 clinically validated patients, ICD codes correctly discovers 39 cachectic cancer patients and misses 122 patients. By contrast, PIES discovers 146 cachectic cancer patients and misses only 15 patients. Conclusions: Pangaea’s AI-driven product for characterizing patients discovered 48% more lung cancer patients, 62% more ovarian cancer patients and 276% more cachectic cancer patients for screening, compared to ICD codes. This includes undiagnosed and misdiagnosed patients. Pangaea’s product platform permits the evolution of knowledge for characterizing different target patient populations through its application across multiple healthcare systems, without the need to move or touch the data, thereby ensuring scalability in a privacy compliant manner.
Demographic and socioeconomic risk factors in treatment of malignant eccrine spiradenoma.
Authors: Won Shin, Jessica Wong, Beau Hsia, Darby Keirns, Peter T. Silberstein, Xinxin Wu
Journal: Journal of Clinical Oncology (2023)

e21595 Background: Malignant Eccrine Spiradenoma (MES) is a rare malignancy that affects the eccrine sweat glands. Although a rare malignancy, MES commonly metastasizes to various regions of the body. Tumor resection with tumor-free margins has a recurrence-free survival of only 23%. There is a lack of analysis on the demographic and socioeconomic factors of MES patients in the literature. Therefore, the National Cancer Database (NCDB) database was utilized in order to address the insufficient epidemiological and demographic information in MES patients. Methods: The National Cancer Database (NCDB) retrospective cohort analysis from 2004 - 2020 included patients with histologically-confirmed diagnosis of MES (N = 188). Regression analysis was used to interpret demographic factors (race, Hispanic origin, facility type, urbanization, income, and Charleson-Deyo score). Results: A total of 188 confirmed cases of MES were identified in the database between 2004 – 2020 with a gradual increase of diagnosed patients per year (R2 = 0.2). The average age of diagnosis was 64.7 years (SD = 15.1, range = 19-90 years). The most common primary site of MES was the skin of the trunk (30.9%), however, primary sites on the skin of upper limb and shoulder (19.1%) and lower limbs and hip (16.5%) were also prevalent. A majority of patients (78.7%) had a Charlson-Deyo comorbidity score of 0. Out of the 188 cases, 83.5% were classified as White. Patients skewed more towards the top quartile in income (30.3%) followed by the second highest quartile (24.5%). The primary source of insurance for patients was Medicare (45.7%) followed by private insurance (41.5%). Most of the patients resided near a metropolitan area (93.6%). The majority of patients were treated in a non-academic facility (59%), rather than an academic facility (41.0%). On average, patients lived 42.3 miles (SD = 184.4, range = 0.6 – 2,319.5) away from their treatment facility. Conclusions: This is the first NCDB analysis on MES, addressing a discrepancy in knowledge on demographic factors contributing to MES. Most MES patients are non-Hispanic and White with their primary site diagnosis being found at the trunk, as seen in case reports. MES patients are more likely to live near a metropolitan area, be treated in a non-academic facility, and have Medicare insurance. More research is imperative to establish an effective treatment protocol for patients with metastatic involvement and to better comprehend the influence of socioeconomic factors on diagnosis and overall survival of patients.
Big data in oncology: Challenges and solutions.
Authors: Neil J. Shah, Nadia Bahadur, Andrew Niederhausern, Alysha Insinga, Ibrahim Shah, Gianna West, Hamza Ahmad, Sene Martin, Adam Watson, Chelsea Nichols, Jasme Lee, Samantha Brown, Jonathan E. Rosenberg, Michael Moran, Gregory J. Riely, Katherine Panageas, Peter D. Stetson, Theodora Bakker, John Philip
Journal: Journal of Clinical Oncology (2023)

6513 Background: Oncology care is complex and often multimodal. With recent technological advances, only a fraction of data is structured feasibly for research. Here we present a step-by-step method of building a novel comprehensive pan-cancer oncology data model using standard data definitions and industry-standard benchmarks. Methods: A team of 133 members was assembled including a project manager, bioinformatic engineers, business analysts, biostatisticians, data stewardship experts, clinical curators, and quality assurance (QA) managers. We first identified data domains that capture a comprehensive patient journey, leveraging existing oncology data models as a starting point, including NAACCR, PRISSMM (Deb Schrag &amp; Eva Lepisto), and mCODE (ASCO). A common data model was developed using standard terms plus 5-10 disease specific elements (DSE). REDCap was used as the database platform, as it is HIPAA compliant and allows customizations. The data was stored in AuroraDB using an architecture and products that provide scalability from both an integration and consumption perspective. Results: We identified 10 data domains, including 186 distinct elements: demographics (20), comorbidities (2); cancer diagnosis &amp; staging (27), pathology (45), imaging (18), medications (11), oncology responses (11), radiation treatments (14), cancer surgeries (11), cancer genomic (19), tumor markers (8), and vitals (8). Standard ontologies were used, including ICD-0-3 histology codes, ICD-10 comorbidities codes, CPT cancer surgeries codes, and CTCAE 5.0 for toxicities. We identified a data steward for each tumor type across medical oncology, surgery, pathology, radiology, and radiation oncology domains who aided curator training and the identification of DSE. QA managers and analysts performed 20% source data verification. In addition, we built REDCap rules (applicable across a form), and complex queries (applicable across multiple forms). To support QA and clinical engagement, interactive Tableau dashboards were constructed. In addition, timing and quality errors were monitored via Tableau dashboards at the individual curator level to provide timely feedback, leading to improved data quality and curation efficiency in real time. The Medical oncology and radiology domains were the most time-consuming, whereas Cancer diagnosis was the most difficult to curate. Conclusions: We collected genomic and phenomic data for 15,579 patients across six tumor types to date. Collecting comprehensive oncology data across tumor types is possible but requires institutional support, collaboration between clinical &amp; informatics teams, and a dedicated QA team. [Table: see text]
The SPEARHEAD-1 trial of afamitresgene autoleucel (afami-cel [formerly ADP-A2M4]): Analysis of overall survival in advanced synovial sarcoma.
Authors: Brian Andrew Van Tine, Kristen N. Ganjoo, Jean-Yves Blay, Claudia Valverde, Dejka M. Araujo, Albiruni Ryan Abdul Razak, Axel Le Cesne, Scott Schuetze, Michael J Wagner, Steven Attia, Edouard Forcade, Mihaela Druta, Seth Pollack, Jane Bai, Paige Bayer, Erin Van Winkle, Elliot Norry, Cheryl McAlpine, Dennis Williams, Sandra P. D'Angelo
Journal: Journal of Clinical Oncology (2023)

11563 Background: Afami-cel is an autologous, T-cell receptor (TCR) T-cell therapy to treat human leukocyte antigen (HLA) A*02–eligible patients (pts) with advanced solid tumors positive for the cancer testis antigen melanoma-associated antigen A4 (MAGE-A4). The efficacy and safety of afami-cel in pretreated pts with advanced/metastatic synovial sarcoma or myxoid/round cell liposarcoma are being evaluated in the Phase 2, two-cohort, open-label, SPEARHEAD-1 (NCT04044768) trial. Here we report interim overall survival (OS) data in the pts with advanced synovial sarcoma in Cohort 1. Methods: Pts who were HLA-A*02 positive with advanced synovial sarcoma (with evidence of clinical/radiological progression) positive for MAGE-A4 received afami-cel after lymphodepleting chemotherapy containing fludarabine and cyclophosphamide. The primary endpoint was overall response rate per RECIST v1.1 by independent review. Secondary endpoints included OS and progression-free survival (PFS), estimated utilizing the Kaplan-Meier method. Results: As of August 29, 2022, 44 pts with synovial sarcoma received afami-cel (2.68–9.99×10 9 transduced T-cells) in Cohort 1. The median age was 41 years (range: 19–73 years), 50% of pts were female, 89% of pts were white, 96% of pts were typed as HLA-A*02:01P, and median tumor MAGE-A4 expression H-score was 257 (132–300). Pts were heavily pre-treated with a median of 3 prior lines of therapy (range: 1–12). The median follow-up time at the data cut-off was 20.8 months. Median PFS was 3.8 months (95% CI: 2.8, 5.8) and 4.1 months (95% CI: 2.8, 6.9) by independent and investigator review, respectively. Median OS by independent review was 15.4 months (95% CI: 10.9, not estimable) with 52% of pts censored at the data cut-off. The 12-month OS probability was 60% and 24-month OS probability was 40%. Twenty-one pts received additional therapy during long term follow up (systemic therapy, n = 20; radiation, n = 6; other, n = 4). In the 17 pts who had a RECIST response by independent review, the median OS was not reached, the 12-month OS probability was 90%, and 24-month OS probability was 60%. Conclusions: Pts with advanced synovial sarcoma treated with afami-cel in SPEARHEAD-1 had encouraging survival, especially those pts with a RECIST response. Clinical trial information: NCT04044768 .
Risk factors for falls and fear of falling among older patients with cancer.
Authors: Sameh Gomaa, Kristine Swartz, Andrew E. Chapman, Tingting Zhan, Kuang-Yi Wen
Journal: Journal of Clinical Oncology (2023)

e24032 Background: Cancer is a disease of the elderly with more than 60% of all cancers diagnosed in adults age 60 or older. Falls pose a substantial risk for older cancer patients leading to hip fractures, traumatic brain injury, and are the leading cause of injury-related hospitalizations in persons aged 60 and older. This risk is due to aging changes compounded by the effect of cancer and cancer therapy. This report aims at understanding risk factors for falls and the perception of fear of falling (FOF) among older patients with cancer. Methods: As a single-site, retrospective cohort study, data was derived from 249 geriatric oncology patients from Senior Adult Oncology Center at Jefferson Health. The primary endpoints are the number of falls in the preceding 12 months and FOF as part of a self-reported fall screening. Multivariate logistic regression model was used to identify the independent risks factors by including patients’ age, sex, race/ethnicity, BMI, polypharmacy, cancer types, ECOG scores, Timed up and go (TUG) test, CARG toxicity scores, Mini-Cog, frailty status and depressive symptom for each endpoint separately. Results: Majorities were White (62.1%) and female (57.2%) with a mean ages of 78.1 (SD = 8.7). The most common cancers diagnosed were hematologic malignancy (34.1%), gastrointestinal (23.4 %) and breast cancer (16.1%). 26.1% experienced one or more falls in the past 12 months and 28.9% were worried about falling. Average CARG score is 10.1 (SD = 3.8). 50.4% had an abnormal TUG tests and 23.6% had an abnormal Mini-Cog. In the multivariate logistic regression, CARG score (OR = 1.46 95% CI = 1.30 - 1.65, p = 0.000) was associated with falls in the past 12 months after adjusting for confounders. Further, both abnormal TUG (OR = 4.63 95% CI = 2.12-10.13 p = 0.000) and increased CARG score (OR = 1.17 95% CI = 1.05-1.31 p = 0.005) were associated with FOF. Conclusions: Falls and FOF are common among older cancer patients in our cohort. As the CARG toxicity score found as a major predictor for prior falls, our findings suggest the importance of future toxicity risk-tailored interventions which could potentially lead to the prevention of further falls. We also found that the TUG test and CARG were associated with FOF, which might diminish older cancer patient’s confidence in participating in daily activities. Future research is needed to investigate causal relationship among comprehensive risk factors and the efficacy of tailored interventions.